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Parameterization of the size-resolved particle activation ratio (AR) is useful for the prediction and analysis of the cloud condensation nuclei (CCN) number concentration ( N CCN ). Critical issues for the application of AR parameterizations in models are (1) the consistency of the different parameterization schemes in terms of fitting AR curves and (2) the applicability of the estimate of N CCN under different aerosol conditions. These issues are discussed in this study based on summertime measurements of the size-resolved AR in the North China Plain. By comparing parameterized AR curves, variations in the fitting parameters and application to N CCN calculations, the consistency of the three existing parameterization schemes is confirmed. Based on an analysis using representative AR fitting parameters, the method commonly used to calculate N CCN with a fixed AR was found to be accurate, except during periods affected by strong black carbon (BC) emissions or new particle formation (NPF) events. The N CCN value was overestimated by approximately 10% when BC aerosol emissions were abundant and deviated from the 1:1 line by 15% during NPF events. The bias of the calculated N CCN due to these significant emission or aerosol production events cannot be eliminated by using the appropriate representative fitting parameters. Under these circumstances, accurate prediction of N CCN requires real-time aerosol hygroscopicity data based on CCN measurements. This research furthers our understanding of the relationship between aerosol cloud activation and aerosol spectra. [ABSTRACT FROM AUTHOR]

Aerosol surface area concentration ( S A ) is crucial for studying atmospheric chemical reactions happened in aerosol water or on aerosol surface. However, there is no commercial instrument that can provide direct measurements of ambient S A . In this paper, we propose a method to calculate ambient S A based only on measurements of a three-wavelength humidified nephelometer system, which measures aerosol optical properties at three wavelengths under dry state and different relative humidity (RH) conditions. Two critical steps are required in this method: (1) Calculating surface area concentration of ambient aerosols in dry state, S A (dry) , using a trained random forest machine learning model based only on optical properties measured by the "dry" nephelometer. The proposed machine learning method is evaluated with particle number size distributions (PNSD) datasets from eight field campaigns conducted on the North China Plain during different seasons. The square of correlation coefficients between predicted and calculated S A (dry) for PNSD is about 0.99, the average ratio between predicted and calculated S A (dry) is 1.01 and 70% of data points has a relative difference less than 10%. (2) Calculating the surface area growth factor f S (RH) of ambient aerosol particles due to water uptake using the proposed f S (RH) parameterization scheme f S (RH) = (1 + κ S R H 100 − R H) 2 3 , and the hygroscopicity parameter κ S can be calculated using measured aerosol light scattering enhancement factor and Å ngstr o ¨ m exponent. The ambient S A values during two field campaigns which are conducted on the North China Plain (Wangdu campaign and Gucheng campaign, in summer and winter respectively) are calculated using the proposed method. The ambient S A ranges from 42 to 1871 μ m 2 / c m 3 with an average of 319 μ m 2 / c m 3 during Wangdu campaign, and ranges from 19 to 4156 μ m 2 / c m 3 with an average of 788 μ m 2 / c m 3 during Gucheng campaign. Drastic daily variations of ambient S A are observed during these two campaigns. The results demonstrate that aerosol hygroscopic growth impacts significantly on variations in ambient S A especially under high RH conditions. The f S (RH) ranges from near 1 to 4 with an average of 1.4 during Wangdu campaign, and ranges from near 1 to 2.7 with an average of 1.3 during Gucheng campaign. The results indicate that the larger RH, the more sensitive f S (RH) becomes to variations in κ S which highlights that real-time measurements of aerosol hygroscopicity are required for accurate calculations of ambient S A . The advantage of the proposed method is that the ambient S A can be obtained solely based on measurements of a three-wavelength humidified nephelometer system, facilitating real-time measurements of ambient S A and promoting studies in aerosol heterogeneous reactions. • Humidified nephelometer system performs great in predicting aerosol surface area. • Proposed method can be used in different regions. • Aerosol hygroscopic growth impacts significantly on aerosol surface area. [ABSTRACT FROM AUTHOR]

Cloud condensation nuclei (CCN) spectra, that indicate the dependence of CCN number concentrations (N CCN) on supersaturation, are important factors in aerosol–cloud interactions. Owing to the lack of direct measurements of N CCN , calculating the CCN spectra based on conventional observational data of aerosols is important to obtain N CCN ; however, this is challenging owing to the complex relationship between aerosol properties and CCN activity. Machine learning techniques have recently been applied to estimate N CCN and found to be a promising method for CCN prediction. In this study, the random forest (RF) model was applied to predict CCN spectral parameters using the observation data measured in four campaigns in the North China Plain, and the effects of chemical and optical properties of aerosol on the estimation of CCN spectra were investigated. The results show that the RF model trained with the data of one campaign can be used to estimate CCN spectral parameters in another campaign, with the coefficient of determination between the estimated and measured CCN spectral parameters being approximately 0.5. The deviations of the estimation by the RF model may result from the difference in both the aerosol properties and measurement uncertainties among different campaigns, whose influence on the deviations can be further magnified by overfitting the RF model. Further analysis revealed that the major aerosol properties among the input variables of the RF model were the mass concentration of black carbon and aerosol hemispheric backscattering fraction at a wavelength of 450 nm. In addition, the roles of chemical compositions of aerosol in estimating CCN spectra parameters are different among different campaigns because the poor correlation between aerosol chemical compositions and CCN spectra parameter can affect the performance of the RF model. For estimating CCN spectra, using aerosol optical properties, including the hemispheric backscattering fraction and absorption coefficient, as model inputs is more recommended than using aerosol chemical properties. • Random forest model is used to estimate CCN spectra based on observation data in the North China Plain. • Mass concentration of black carbon and hemispheric backscattering fraction are the most important input variables. • Aerosol optical properties data are recommended in estimation of CCN spectra parameters. [ABSTRACT FROM AUTHOR]

Particle microphysical and chemical properties, including particle number size distribution (PNSD) and chemical composition of both ambient PM 1 and PM 10 particles were measured in the North China Plain (NCP) during the Multiphase chemistry experiment in Fogs and Aerosols in the North China Plain (McFAN) campaign in November 2018. Focused on the differences of measured PNSD between PM 1 and PM 10 , the variations of aerosol microphysical and chemical properties in fog were analyzed and their corresponding influence on fog droplets evolution was evaluated. Two nighttime fog events were observed on the 12th and 13th November, respectively. During fog events, PNSDs of particles with diameter larger than ∼200 nm sampled from the PM 10 inlet were similar to those after fogs. But in particle size range larger than 200 nm, particles number concentration sampled from the PM 1 inlet were very few and much lower than those after fogs, indicating that some particles larger than 200 nm have grown into droplets with diameter larger than 1 μm and thus were not detected under the PM 1 inlet. No significant decrease in particle number concentration larger than ∼200 nm was detected under the PM 10 inlet after the dissipation of these fog events, suggesting that fog droplets were generally smaller than 10 μm and could be sampled by the PM 10 inlet. The additional particles sampled in PM 10 in fogs demonstrated a normal distribution, which indicates that they belonged to an individual particle group externally mixed with those sampled under the PM 1 impactor and could outgrow 1 μm either through hygroscopic growth or through activation. This can lead to uncertainties in droplet number concentration estimations within fog from microphysical and chemical properties measurements of the aerosol with assumption of fully internal mixture. We find that PM 1 hygroscopicity derived from particle chemical composition measurements was overestimated, which may result in significant bias in the estimated droplets number concentration in the North China Plain. These results and approaches in this study can improve the understanding of aerosol microphysical properties in fogs and the corresponding influence of aerosol particles on fog microphysics. [Display omitted] • Simultaneous measurements of PM1 and PM10 during fog events in the North China Plain. • Differences between PNSDs of PM1 and PM10 suggest additional particles in PM10 different from PM1. • Ignoring external mixing state of PM10 leads to an overestimated droplet number concentration. [ABSTRACT FROM AUTHOR]